{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T19:57:10Z","timestamp":1785527830792,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,12,30]],"date-time":"2016-12-30T00:00:00Z","timestamp":1483056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61303003"],"award-info":[{"award-number":["61303003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41374113"],"award-info":[{"award-number":["41374113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National High-tech R&amp;D (863) Program of China","award":["2013AA01A208"],"award-info":[{"award-number":["2013AA01A208"]}]},{"name":"Tsinghua University Initiative Scientific Research Program","award":["20131089356"],"award-info":[{"award-number":["20131089356"]}]},{"name":"National Key Research and Development Plan of China","award":["2016YFA0602200"],"award-info":[{"award-number":["2016YFA0602200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study.<\/jats:p>","DOI":"10.3390\/rs9010022","type":"journal-article","created":{"date-parts":[[2016,12,30]],"date-time":"2016-12-30T08:48:53Z","timestamp":1483087733000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":334,"title":["Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1838-9176","authenticated-orcid":false,"given":"Weijia","family":"Li","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China"},{"name":"Joint Center for Global Change Studies (JCGCS), Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haohuan","family":"Fu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China"},{"name":"Joint Center for Global Change Studies (JCGCS), Beijing 100084, China"},{"name":"National Supercomputing Center in Wuxi, Wuxi 214072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3115-2042","authenticated-orcid":false,"given":"Le","family":"Yu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China"},{"name":"Joint Center for Global Change Studies (JCGCS), Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arthur","family":"Cracknell","sequence":"additional","affiliation":[{"name":"Division of Electronic Engineering and Physics, University of Dundee, Dundee DDI 4HN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4400","DOI":"10.15376\/biores.7.3.4400-4423","article-title":"A review of oil palm biocomposites for furniture design and applications: Potential and challenges","volume":"7","author":"Suhaily","year":"2012","journal-title":"BioResources"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4692","DOI":"10.1109\/JSTARS.2014.2331425","article-title":"Efficient framework for palm tree detection in UAV images","volume":"7","author":"Malek","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7400","DOI":"10.1080\/01431161.2013.820367","article-title":"Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery","volume":"34","author":"Cracknell","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1177\/0309133312452187","article-title":"A review of remote sensing based productivity models and their suitability for studying oil palm productivity in tropical regions","volume":"36","author":"Tan","year":"2012","journal-title":"Prog. Phys. Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7424","DOI":"10.1080\/01431161.2013.822601","article-title":"Use of UK-DMC2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia","volume":"34","author":"Tan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kanniah, K.D., Tan, K.P., and Cracknell, A.P. (2012, January 22\u201327). UK-DMC2 satellite data for deriving biophysical parameters of oil palm trees in Malaysia. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352094"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5431","DOI":"10.1080\/01431161.2016.1241448","article-title":"Oil palm mapping using Landsat and PALSAR: A case study in Malaysia","volume":"37","author":"Cheng","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/01431161.2014.995278","article-title":"Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees","volume":"36","author":"Cracknell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/S0034-4257(02)00050-0","article-title":"Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration","volume":"82","author":"Pouliot","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1080\/01431161003662928","article-title":"Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery","volume":"32","author":"Shafri","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","article-title":"A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9749","DOI":"10.3390\/rs6109749","article-title":"Oil palm tree detection with high resolution multi-spectral satellite imagery","volume":"6","author":"Srestasathiern","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"465","DOI":"10.5194\/isprs-annals-III-3-465-2016","article-title":"Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix","volume":"III-3","author":"Manandhar","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Rhode Island, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 24\u201327). Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., Shen, X., Brandt, J., and Hua, G. (2015, January 7\u201312). A convolutional neural network cascade for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299170"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ouyang, W., and Wang, X. (2013, January 1\u20138). Joint deep learning for pedestrian detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.257"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zeng, X., Ouyang, W., and Wang, X. (2013, January 1\u20138). Multi-stage contextual deep learning for pedestrian detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.22"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5632","DOI":"10.1080\/01431161.2016.1246775","article-title":"Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping","volume":"37","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_28","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_29","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/arxiv.org\/abs\/1603.04467."},{"key":"ref_30","unstructured":"Bradski, G., and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O\u2019Reilly Media, Inc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:36Z","timestamp":1760210976000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,30]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010022"],"URL":"https:\/\/doi.org\/10.3390\/rs9010022","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,30]]}}}